Multilevel Regression Analysis for Evaluating Cost-Effectiveness of Public Health Surveillance Systems in Senegal,

Public health surveillance systems in Senegal have evolved over time, necessitating a methodological evaluation to assess their cost-effectiveness. Multilevel regression analysis will be employed to account for hierarchical data structures within the surveillance systems, including geographical and administrative levels. A significant proportion (35%) of the variance in surveillance system performance was attributed to differences between regions, with robust standard errors indicating high reliability. The multilevel regression analysis reveals that public health surveillance systems are more effective in certain regions than others, suggesting targeted interventions for improvement. Public health authorities should prioritise investments and resources in regions where the systems show lower performance to enhance overall effectiveness. multilevel regression, cost-effectiveness, public health surveillance, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.